| 1 | # Read thesis-append-pbv.csv
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| 2 | # Output for string-graph-peq-sharing.dat
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| 3 |
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| 4 | # Project details
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| 5 | # Filter operation=peq
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| 6 | # Split "series" goups of sut; only those in the "pretty" list
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| 7 | # Assert one row per string-length
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| 8 | # output:
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| 9 | # string-len op-duration
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| 10 | # in chunks, each headed by pertty(sut)
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| 11 |
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| 12 | import pandas as pd
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| 13 | import numpy as np
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| 14 | import os
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| 15 | import sys
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| 16 |
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| 17 | sys.path.insert(0, os.path.dirname(__file__))
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| 18 | from common import *
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| 19 |
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| 20 | # re: apparent cherrypicking
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| 21 | # The system's response to the liveness threshold is not smooth.
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| 22 | # The system only uses the threshold to decide whether it will double the text heap again or not.
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| 23 | # The system's speed for a given string size in a given amount of memory is not affected by the specific value of the liveness threshold.
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| 24 | # Goals with this selection are
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| 25 | # - showing one speed result per <string size, memory usage amount>
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| 26 | # - cropping diminishing or negative returns for large memory sizes
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| 27 | # - diminishing is obvious, already shown past chosen sweet spot in this selection
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| 28 | # - negative caused by overflowing llc, not relevant to sting impl
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| 29 | favSizes = {20:[-1.0, 0.05, 0.1, 0.2, 0.5, 0.9],
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| 30 | 50:[-1.0, 0.05, 0.1, 0.2, 0.5, 0.9],
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| 31 | 100:[-1.0, 0.1, 0.2, 0.5, 0.9],
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| 32 | 200:[-1.0, 0.1, 0.2, 0.5, 0.9],
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| 33 | 500:[-1.0, 0.2, 0.4, 0.9, 0.98]}
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| 34 |
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| 35 | defaultExpansions = [-1, 0.2]
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| 36 |
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| 37 | cfatimings = loadParseTimingData('result-allocate-speed-cfa.csv',
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| 38 | xClasNames=['expansion'], xClasDtypes={'expansion':'Float64'},
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| 39 | xFactNames=['topIters'], xFactDtypes={'topIters':np.int64})
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| 40 |
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| 41 | cfasizings = loadParseSizingData('result-allocate-space-cfa.ssv', xClasNames=['expansion'], xClasDtypes={'expansion':'Float64'})
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| 42 |
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| 43 | stltimings = loadParseTimingData('result-allocate-speed-stl.csv',
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| 44 | xClasNames=['expansion'], xClasDtypes={'expansion':'Float64'},
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| 45 | xFactNames=['topIters'], xFactDtypes={'topIters':np.int64})
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| 46 |
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| 47 | stlsizings = loadParseSizingData('result-allocate-space-stl.ssv', xClasNames=['expansion'], xClasDtypes={'expansion':'Float64'})
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| 48 |
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| 49 | timings = pd.concat([cfatimings, stltimings])
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| 50 | sizings = pd.concat([cfasizings, stlsizings])
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| 51 |
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| 52 | combined = pd.merge(
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| 53 | left=timings,
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| 54 | right=sizings[['sut', 'corpus','expansion','hw_cur_req_mem(B)']],
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| 55 | on=['sut', 'corpus','expansion']
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| 56 | )
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| 57 |
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| 58 | combined = combined.pivot_table( values=['op-duration-ns','hw_cur_req_mem(B)'], index=['corpus-meanlen-tgt', 'sut-platform', 'expansion'], aggfunc=['mean', 'min', 'max'] )
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| 59 | combined = combined.reset_index()
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| 60 | combined.columns = combined.columns.to_flat_index()
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| 61 |
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| 62 | # text = combined.to_csv(header=True, index=True, sep='\t')
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| 63 | # print(text)
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| 64 |
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| 65 |
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| 66 | combined['is-default'] = np.isin(combined[('expansion','')], defaultExpansions).astype(int)
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| 67 |
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| 68 |
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| 69 |
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| 70 | # print ('!!')
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| 71 | # print(combined)
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| 72 |
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| 73 |
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| 74 | # Emit
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| 75 |
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| 76 | # First, for the CFA curves
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| 77 | sut = "cfa"
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| 78 | sutGroup = combined.groupby(('sut-platform','')).get_group(sut)
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| 79 |
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| 80 | groupedSize = sutGroup.groupby(('corpus-meanlen-tgt',''))
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| 81 |
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| 82 | for sz, szgroup in groupedSize:
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| 83 |
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| 84 | if sz in favSizes.keys():
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| 85 | szgroup_sorted = szgroup.sort_values(by=('expansion',''))
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| 86 |
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| 87 | print('"{sut}, len={len}"'.format(sut=sut, len=sz))
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| 88 | # print(szgroup_sorted) ##
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| 89 | # print(szgroup_sorted['expansion'], 'isin', favSizes[sz]) ##
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| 90 | favoured = szgroup_sorted.loc[szgroup_sorted[('expansion','')].isin(favSizes[sz])]
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| 91 | # print('!') ##
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| 92 | # print(favoured) ##
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| 93 | text = favoured[[('expansion',''),
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| 94 | ('mean','op-duration-ns'),
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| 95 | ('min','op-duration-ns'),
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| 96 | ('max','op-duration-ns'),
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| 97 | ('mean', 'hw_cur_req_mem(B)'),
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| 98 | ('min', 'hw_cur_req_mem(B)'),
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| 99 | ('max', 'hw_cur_req_mem(B)'),
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| 100 | 'is-default']].to_csv(header=False, index=False, sep='\t')
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| 101 | print(text)
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| 102 | print()
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| 103 |
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| 104 | # Again, for the STL-comparisons, default expansion only
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| 105 |
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| 106 | atDefaults = combined.groupby('is-default').get_group(1)
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| 107 |
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| 108 | for sz, szgroup in atDefaults.groupby(('corpus-meanlen-tgt','')):
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| 109 |
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| 110 | if sz in favSizes.keys():
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| 111 | print(sz)
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| 112 | text = szgroup[[('expansion',''),
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| 113 | ('mean','op-duration-ns'),
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| 114 | ('min','op-duration-ns'),
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| 115 | ('max','op-duration-ns'),
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| 116 | ('mean', 'hw_cur_req_mem(B)'),
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| 117 | ('min', 'hw_cur_req_mem(B)'),
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| 118 | ('max', 'hw_cur_req_mem(B)'),
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| 119 | ('sut-platform','')]].to_csv(header=False, index=False, sep='\t')
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| 120 | print(text)
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| 121 | print()
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